contrast_of_interest = 'P_VC_STIM_stimlin_high_gt_low'
contrast_of_interest = 'P_VC_STIM_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 2.865215 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6854064 Bit rate: 22.71 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 4 participants, size is now 68
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0051" "participants that are outliers:... sub-0074" "participants that are outliers:... sub-0093" "participants that are outliers:... sub-0129"
disp(n);
{'sub-0051'} {'sub-0074'} {'sub-0093'} {'sub-0129'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain 0.0333 5.2969 0.0000 1.0000 ***
Cog Wholebrain 0.0253 6.0761 0.0000 1.0000 ***
Emo Wholebrain -0.0542 -8.4892 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} 0.033329 0.0062838 5.304 1.2257e-06 0.62508
{'Cog Wholebrain' } 0.025337 0.0041678 6.0791 5.4475e-08 0.71643
{'Emo Wholebrain' } -0.054238 0.0063476 -8.5446 1.6345e-12 -1.007
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [9.0016 10.0016 11.0016]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} 0.033592 0.0058742 5.7185 2.3585e-07 0.67393
{'Cog Wholebrain' } 0.022271 0.0036473 6.1062 4.876e-08 0.71962
{'Emo Wholebrain' } -0.052702 0.0059828 -8.8089 5.2947e-13 -1.0381
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [12.0016 13.0016 14.0016]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'V_PC_STIM_stimlin_high_gt_low'
contrast_of_interest = 'V_PC_STIM_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 2.479252 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6879209 Bit rate: 22.71 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 69
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0051" "participants that are outliers:... sub-0074" "participants that are outliers:... sub-0094"
disp(n);
{'sub-0051'} {'sub-0074'} {'sub-0094'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0180 -2.8387 0.0059 1.0000 **
Cog Wholebrain -0.0052 -1.0603 0.2926 0.0000
Emo Wholebrain 0.0217 3.1521 0.0024 1.0000 **
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ _________ ________
{'Pain Wholebrain'} -0.018045 0.006358 -2.8382 0.0059118 -0.33448
{'Cog Wholebrain' } -0.0051663 0.0048719 -1.0604 0.29254 -0.12497
{'Emo Wholebrain' } 0.021706 0.0068793 3.1553 0.0023518 0.37186
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [9.0017 10.0017 11.0017]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.019601 0.0061957 -3.1637 0.0022933 -0.37285
{'Cog Wholebrain' } -0.0044415 0.0044477 -0.9986 0.32138 -0.11769
{'Emo Wholebrain' } 0.022872 0.0066404 3.4444 0.00096443 0.40592
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [12.0017 13.0017 14.0017]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'C_PV_STIM_stimlin_high_gt_low'
contrast_of_interest = 'C_PV_STIM_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 2.549703 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6874480 Bit rate: 22.71 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 1 participants, size is now 71
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
participants that are outliers:... sub-0084
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0169 -2.9775 0.0040 1.0000 **
Cog Wholebrain -0.0256 -5.4650 0.0000 1.0000 ***
Emo Wholebrain 0.0389 7.1605 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.016868 0.0056683 -2.9759 0.0039915 -0.35071
{'Cog Wholebrain' } -0.025608 0.0046833 -5.4679 6.4219e-07 -0.64439
{'Emo Wholebrain' } 0.0389 0.0054249 7.1707 5.7051e-10 0.84508
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [9.0018 10.0018 11.0018]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.017625 0.0053762 -3.2783 0.0016186 -0.38636
{'Cog Wholebrain' } -0.024587 0.0044365 -5.542 4.7822e-07 -0.65313
{'Emo Wholebrain' } 0.039538 0.0051807 7.6318 8.0427e-11 0.89942
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [12.0018 13.0018 14.0018]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'motor'
contrast_of_interest = 'motor'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 2.501854 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6888883 Bit rate: 22.72 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 2 participants, size is now 70
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0044" "participants that are outliers:... sub-0088"
disp(n);
{'sub-0044'} {'sub-0088'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0082 -1.8276 0.0718 0.0000
Cog Wholebrain 0.0299 11.6792 0.0000 1.0000 ***
Emo Wholebrain -0.0190 -4.3232 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.008244 0.0045121 -1.8271 0.071892 -0.21532
{'Cog Wholebrain' } 0.029943 0.002562 11.687 2.2204e-15 1.3774
{'Emo Wholebrain' } -0.019015 0.0043979 -4.3236 4.9163e-05 -0.50955
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [9.0020 10.0020 11.0020]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.0041255 0.004078 -1.0117 0.31514 -0.11923
{'Cog Wholebrain' } 0.026864 0.002331 11.524 2.2204e-15 1.3582
{'Emo Wholebrain' } -0.020797 0.0040213 -5.1717 2.0547e-06 -0.60949
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [12.0020 13.0020 14.0020]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'P_VC_STIM'
contrast_of_interest = 'P_VC_STIM'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 2.482658 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6904015 Bit rate: 22.72 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 2 participants, size is now 70
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0092" "participants that are outliers:... sub-0098"
disp(n);
{'sub-0092'} {'sub-0098'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain 0.1854 26.5518 0.0000 1.0000 ***
Cog Wholebrain 0.0103 2.8627 0.0055 1.0000 **
Emo Wholebrain -0.1847 -29.7366 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} 0.18538 0.006831 27.137 2.2204e-15 3.1982
{'Cog Wholebrain' } 0.01027 0.0035866 2.8633 0.0055068 0.33745
{'Emo Wholebrain' } -0.18465 0.0060659 -30.441 2.2204e-15 -3.5875
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [9.0021 10.0021 11.0021]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} 0.18143 0.0072074 25.173 2.2204e-15 2.9667
{'Cog Wholebrain' } 0.0084672 0.0033688 2.5134 0.014227 0.29621
{'Emo Wholebrain' } -0.18163 0.0065448 -27.751 2.2204e-15 -3.2705
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [12.0021 13.0021 14.0021]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'V_PC_STIM'
contrast_of_interest = 'V_PC_STIM'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 2.492377 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6867957 Bit rate: 22.71 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 1 participants, size is now 71
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
participants that are outliers:... sub-0107
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.1298 -25.4504 0.0000 1.0000 ***
Cog Wholebrain -0.0020 -0.5615 0.5762 0.0000
Emo Wholebrain 0.1246 24.3087 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ __________ _________
{'Pain Wholebrain'} -0.12976 0.0050435 -25.728 2.2204e-15 -3.0321
{'Cog Wholebrain' } -0.0020222 0.0035943 -0.56261 0.57547 -0.066304
{'Emo Wholebrain' } 0.12463 0.0050823 24.522 2.2204e-15 2.8899
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [9.0022 10.0022 11.0022]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ __________ _________
{'Pain Wholebrain'} -0.12437 0.0051107 -24.335 2.2204e-15 -2.8679
{'Cog Wholebrain' } -0.0020334 0.0032879 -0.61846 0.53825 -0.072886
{'Emo Wholebrain' } 0.12103 0.0050026 24.193 2.2204e-15 2.8512
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [12.0022 13.0022 14.0022]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'C_PV_STIM'
contrast_of_interest = 'C_PV_STIM'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 3.155071 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6921663 Bit rate: 22.72 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 2 participants, size is now 70
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0089" "participants that are outliers:... sub-0107"
disp(n);
{'sub-0089'} {'sub-0107'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0620 -15.8666 0.0000 1.0000 ***
Cog Wholebrain -0.0081 -2.7434 0.0077 1.0000 **
Emo Wholebrain 0.0659 16.0341 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.062008 0.0038996 -15.901 2.2204e-15 -1.874
{'Cog Wholebrain' } -0.0080503 0.0029345 -2.7433 0.0076946 -0.3233
{'Emo Wholebrain' } 0.065899 0.0040986 16.078 2.2204e-15 1.8949
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [9.0023 10.0023 11.0023]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.060369 0.0037952 -15.906 2.2204e-15 -1.8746
{'Cog Wholebrain' } -0.0064663 0.0028433 -2.2742 0.025979 -0.26802
{'Emo Wholebrain' } 0.063796 0.0041164 15.498 2.2204e-15 1.8265
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [12.0023 13.0023 14.0023]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
% pubfilename = '6cond_cueeffect_contrast.mlx';
% p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
% 'format', 'html', 'outputDir', pubdir, ...
% 'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
% htmlfile = publish(pubfilename, p);